Variance Risk Premium Components in Japan for Predictability: Evidence from the COVID-19 Pandemic
Bibliographic record
Abstract
The literature on asset predictability suggests the usefulness of the variance risk premium (VRP) and its diffusive and jump risk components as predictors that can yield an improved forecast power. This study investigates whether there is a robust and statistically significant relation between the VRP components and the future Japanese composite index of coincident indicators (CI) and credit spreads (CS), including the outbreak of the COVID-19 pandemic which has caused economic conditions and financial markets to become unstable. The main empirical results are as follows: (i) our rolling window predictive regressions indicate the stability of the significantly negative relation between the diffusive risk component of the VRP and the future CI; (ii) the significantly positive relation of the jump risk component of the VRP and the future lower-rated CS is hampered by the inclusion of the COVID-19 period when the Bank of Japan purchased large-scale corporate bonds under the continuing Japanese expansionary monetary policy; and (iii) the diffusive risk component is partly affected by the impact of the COVID-19 pandemic, but remains significantly positive relation with the future higher- and lower-rated CS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".